Towards Execution-Grounded Automated AI Research — part 4/4

Cleaned-text code points [47021, 48825). Bibliography omitted here; source.md includes it.

[Experiment] Implement response diversity rewards within groups where responses to the same prompt receive bonus rewards (0.05-0.15) for being dissimilar to other responses in their group, encouraging exploration of different solution paths while maintaining the proven group_size=8 and 3e-5 learning rate combination.

[Code Changes] Modify compute_group_normalized_rewards in grpo_utils.py to compute pairwise similarity between responses in each group using token-level Jaccard similarity. Add diversity bonus: diversity_reward = 0.15 * (1 - max_similarity_in_group) to each response’s reward before advantage computation. Reshape responses into groups, compute similarities, and add bonuses before advantage normalization. Set --learning_rate 3e-5 , --loss_type reinforce_with_baseline , --group_size 8 .

Validation Accuracy: 19.2

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